Devices are known, such as laptop computers, desktop computers, cell phones, cable set top boxes, printers and other devices, that employ graphics processing that may be used to perform filtering operations on digital representations of images. Software programs for performing such filtering are also well known. A typical filtering operation performed on images is noise reduction. When an image is digitally captured, an inevitable consequence of the capture process is the addition of random “noise” that degrades, no matter how slightly, the quality of the captured image.
In the broadest sense of the term “filtering,” the value of the filtered image at a given location is a function of the values of the input image in a relatively small neighborhood of the same location. Typically, a low-pass filter is used that computes an average of pixel values (often a weighted average in which each weight is determined by a Gaussian function) in the neighborhood of a particular pixel being filtered. Low-pass filters are effective in filtering out additive noise because, intuitively, images typically vary slowly over space, so nearby pixels are likely to have similar color or gray values. Therefore it is appropriate to average them together. The noise values that corrupt a pixel and its nearby pixels are mutually less correlated than the pixel values, so noise is averaged away while signal is preserved. However, the assumption of slow spatial variations fails at edges (i.e., boundaries between regions of substantially different colors within the image), which are consequently blurred by low-pass filtering.
To overcome the edge blurring effect of low-pass filtering schemes, Tomasi and Manduchi originally proposed the concept of bilateral filters that preserve the noise-reduction properties of spatial low-pass filters while simultaneously reducing the edge blurring effect of such filters. The Tomasi/Manduchi bilateral filter combines two low-pass filtering operations: spatial, which averages pixels together based on their geometric distance from the pixel being filtered, and photometric, which averages pixels based upon the perceptual similarity between the currently filtered (target) pixel and the pixels in its vicinity. Thus, the spatial low-pass filter component of the bilateral filter provides the desired noise reduction, whereas the photometric low-pass filter component preserves edges by more heavily weighting nearby pixels that are similar in color to the target pixel and reducing the effect of nearby pixels that are dissimilar in color to the target pixel. Stated more succinctly, the bilateral filter exploits the concept that it makes sense to most heavily rely upon those pixels that are geometrically close and similar in color when averaging away the noise signal.
In practice, the bilateral filter has been shown to provide very good noise reduction performance while preserving the integrity of edges found in the image. However, it would be desirable to not only preserve edges, but to actually enhance (sharpen) edges found in digitally represented images. For example, the use of cameras in cellular telephones has increasingly gained consumer acceptance. However, such camera systems often suffer from relatively low quality optics and light sensing components, which subsequently tend to increase the noise level in captured images. Furthermore, edge blurring in images captured by such systems tends to be worsened by the relatively low quality optics and by shaking of the camera during long exposure times. Accordingly, it would be advantageous to provide edge sharpening capabilities without amplification of noise or, alternatively, edge sharpening capabilities in addition to noise filtering operations in systems operating upon captured digital images.